Overview

Dataset statistics

Number of variables17
Number of observations169302
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.0 MiB
Average record size in memory136.0 B

Variable types

NUM13
BOOL2
CAT2

Warnings

name has a high cardinality: 132939 distinct values High cardinality
artists has a high cardinality: 33375 distinct values High cardinality
name is uniformly distributed Uniform
df_index has unique values Unique
instrumentalness has 46055 (27.2%) zeros Zeros
key has 21431 (12.7%) zeros Zeros
popularity has 26802 (15.8%) zeros Zeros

Reproduction

Analysis started2020-09-16 13:32:12.127976
Analysis finished2020-09-16 13:32:52.102698
Duration39.97 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct169302
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84849.84659
Minimum0
Maximum169908
Zeros1
Zeros (%)< 0.1%
Memory size1.3 MiB
2020-09-16T21:32:52.426172image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8474.05
Q142392.25
median84813.5
Q3127255.75
95-th percentile161353.95
Maximum169908
Range169908
Interquartile range (IQR)84863.5

Descriptive statistics

Standard deviation49030.07791
Coefficient of variation (CV)0.5778452157
Kurtosis-1.198528767
Mean84849.84659
Median Absolute Deviation (MAD)42432
Skewness0.0024169362
Sum1.436524873e+10
Variance2403948540
MonotocityStrictly increasing
2020-09-16T21:32:52.665920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
1590791< 0.1%
 
1631731< 0.1%
 
1611241< 0.1%
 
1508831< 0.1%
 
1488341< 0.1%
 
1549771< 0.1%
 
1529281< 0.1%
 
443511< 0.1%
 
423021< 0.1%
 
Other values (169292)169292> 99.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
1699081< 0.1%
 
1699071< 0.1%
 
1699061< 0.1%
 
1699051< 0.1%
 
1699041< 0.1%
 

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct132939
Distinct (%)78.5%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
Summertime
 
62
Overture
 
43
Home
 
40
Stay
 
34
You
 
33
Other values (132934)
169090 
ValueCountFrequency (%) 
Summertime62< 0.1%
 
Overture43< 0.1%
 
Home40< 0.1%
 
Stay34< 0.1%
 
You33< 0.1%
 
I Love You32< 0.1%
 
Forever32< 0.1%
 
Angel31< 0.1%
 
Paradise31< 0.1%
 
Runaway30< 0.1%
 
Other values (132929)16893499.8%
 
2020-09-16T21:32:53.435539image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique113871 ?
Unique (%)67.3%
2020-09-16T21:32:53.616199image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length255
Median length18
Mean length23.50569987
Min length1

artists
Categorical

HIGH CARDINALITY

Distinct33375
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
['Эрнест Хемингуэй']
 
1215
['Francisco Canaro']
 
938
['Эрих Мария Ремарк']
 
781
['Ignacio Corsini']
 
620
['Frank Sinatra']
 
592
Other values (33370)
165156 
ValueCountFrequency (%) 
['Эрнест Хемингуэй']12150.7%
 
['Francisco Canaro']9380.6%
 
['Эрих Мария Ремарк']7810.5%
 
['Ignacio Corsini']6200.4%
 
['Frank Sinatra']5920.3%
 
['Bob Dylan']5390.3%
 
['The Rolling Stones']5120.3%
 
['Johnny Cash']5010.3%
 
['The Beach Boys']4910.3%
 
['Elvis Presley']4880.3%
 
Other values (33365)16262596.1%
 
2020-09-16T21:32:53.899920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique19627 ?
Unique (%)11.6%
2020-09-16T21:32:54.057331image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length661
Median length17
Mean length23.24452753
Min length5

duration_ms
Real number (ℝ≥0)

Distinct50212
Distinct (%)29.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231435.0531
Minimum5108
Maximum5403500
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-09-16T21:32:54.210222image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5108
5-th percentile112667
Q1171187
median208621
Q3262907
95-th percentile409640
Maximum5403500
Range5398392
Interquartile range (IQR)91720

Descriptive statistics

Standard deviation121133.4509
Coefficient of variation (CV)0.523401487
Kurtosis117.0778563
Mean231435.0531
Median Absolute Deviation (MAD)44088
Skewness6.528090803
Sum3.918241735e+10
Variance1.467331293e+10
MonotocityNot monotonic
2020-09-16T21:32:54.353749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
19200055< 0.1%
 
18000053< 0.1%
 
18600050< 0.1%
 
24000050< 0.1%
 
18400049< 0.1%
 
16000047< 0.1%
 
16800045< 0.1%
 
16900045< 0.1%
 
17000044< 0.1%
 
17500044< 0.1%
 
Other values (50202)16882099.7%
 
ValueCountFrequency (%) 
51081< 0.1%
 
59911< 0.1%
 
63621< 0.1%
 
64671< 0.1%
 
88532< 0.1%
 
ValueCountFrequency (%) 
54035001< 0.1%
 
42700341< 0.1%
 
42694071< 0.1%
 
41202582< 0.1%
 
38163731< 0.1%
 

year
Real number (ℝ≥0)

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1977.330362
Minimum1921
Maximum2020
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-09-16T21:32:54.501954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1921
5-th percentile1935
Q11957
median1978
Q31999
95-th percentile2016
Maximum2020
Range99
Interquartile range (IQR)42

Descriptive statistics

Standard deviation25.54741476
Coefficient of variation (CV)0.01292015499
Kurtosis-1.021059253
Mean1977.330362
Median Absolute Deviation (MAD)21
Skewness-0.1368376892
Sum334765985
Variance652.6704007
MonotocityNot monotonic
2020-09-16T21:32:54.656848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
199320001.2%
 
199620001.2%
 
199420001.2%
 
196620001.2%
 
199220001.2%
 
196220001.2%
 
199020001.2%
 
198920001.2%
 
198820001.2%
 
198620001.2%
 
Other values (90)14930288.2%
 
ValueCountFrequency (%) 
19211280.1%
 
192272< 0.1%
 
19231690.1%
 
19242370.1%
 
19252630.2%
 
ValueCountFrequency (%) 
202017411.0%
 
201920001.2%
 
201819991.2%
 
201719991.2%
 
201619691.2%
 

acousticness
Real number (ℝ≥0)

Distinct4714
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4917566408
Minimum0
Maximum0.996
Zeros21
Zeros (%)< 0.1%
Memory size1.3 MiB
2020-09-16T21:32:54.810608image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.00143
Q10.093625
median0.489
Q30.886
95-th percentile0.992
Maximum0.996
Range0.996
Interquartile range (IQR)0.792375

Descriptive statistics

Standard deviation0.3762684712
Coefficient of variation (CV)0.7651517844
Kurtosis-1.612390863
Mean0.4917566408
Median Absolute Deviation (MAD)0.396
Skewness0.01405904966
Sum83255.3828
Variance0.1415779624
MonotocityNot monotonic
2020-09-16T21:32:54.949030image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.99529711.8%
 
0.99422451.3%
 
0.99316971.0%
 
0.99214660.9%
 
0.99112390.7%
 
0.9911560.7%
 
0.99610310.6%
 
0.98910220.6%
 
0.9888960.5%
 
0.9878000.5%
 
Other values (4704)15477991.4%
 
ValueCountFrequency (%) 
021< 0.1%
 
1e-061< 0.1%
 
1.01e-063< 0.1%
 
1.03e-061< 0.1%
 
1.05e-062< 0.1%
 
ValueCountFrequency (%) 
0.99610310.6%
 
0.99529711.8%
 
0.99422451.3%
 
0.99316971.0%
 
0.99214660.9%
 

danceability
Real number (ℝ≥0)

Distinct1232
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5386953793
Minimum0
Maximum0.988
Zeros146
Zeros (%)0.1%
Memory size1.3 MiB
2020-09-16T21:32:55.097276image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.233
Q10.418
median0.549
Q30.668
95-th percentile0.813
Maximum0.988
Range0.988
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.1751954374
Coefficient of variation (CV)0.3252217192
Kurtosis-0.4202513441
Mean0.5386953793
Median Absolute Deviation (MAD)0.124
Skewness-0.2156799167
Sum91202.2051
Variance0.03069344128
MonotocityNot monotonic
2020-09-16T21:32:55.499994image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.5654360.3%
 
0.5784110.2%
 
0.6124100.2%
 
0.5594010.2%
 
0.5563990.2%
 
0.6223970.2%
 
0.613970.2%
 
0.6023950.2%
 
0.5463910.2%
 
0.5453900.2%
 
Other values (1222)16527597.6%
 
ValueCountFrequency (%) 
01460.1%
 
0.05511< 0.1%
 
0.05592< 0.1%
 
0.05621< 0.1%
 
0.05692< 0.1%
 
ValueCountFrequency (%) 
0.9881< 0.1%
 
0.9862< 0.1%
 
0.9851< 0.1%
 
0.9821< 0.1%
 
0.983< 0.1%
 

energy
Real number (ℝ≥0)

Distinct2332
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4895964759
Minimum0
Maximum1
Zeros10
Zeros (%)< 0.1%
Memory size1.3 MiB
2020-09-16T21:32:55.653248image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.077505
Q10.264
median0.482
Q30.71075
95-th percentile0.925
Maximum1
Range1
Interquartile range (IQR)0.44675

Descriptive statistics

Standard deviation0.267074182
Coefficient of variation (CV)0.5454985791
Kurtosis-1.096355636
Mean0.4895964759
Median Absolute Deviation (MAD)0.223
Skewness0.07442018773
Sum82889.66256
Variance0.07132861867
MonotocityNot monotonic
2020-09-16T21:32:55.798112image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.282350.1%
 
0.4592340.1%
 
0.2542330.1%
 
0.3412320.1%
 
0.3062320.1%
 
0.2992310.1%
 
0.312300.1%
 
0.322300.1%
 
0.2452300.1%
 
0.2192270.1%
 
Other values (2322)16698898.6%
 
ValueCountFrequency (%) 
010< 0.1%
 
1.99e-052< 0.1%
 
2.01e-056< 0.1%
 
2.02e-055< 0.1%
 
2.03e-0514< 0.1%
 
ValueCountFrequency (%) 
120< 0.1%
 
0.99925< 0.1%
 
0.99838< 0.1%
 
0.99756< 0.1%
 
0.99667< 0.1%
 

instrumentalness
Real number (ℝ≥0)

ZEROS

Distinct5401
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1604745519
Minimum0
Maximum1
Zeros46055
Zeros (%)27.2%
Memory size1.3 MiB
2020-09-16T21:32:55.944436image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.000197
Q30.083275
95-th percentile0.903
Maximum1
Range1
Interquartile range (IQR)0.083275

Descriptive statistics

Standard deviation0.3079964242
Coefficient of variation (CV)1.919285149
Kurtosis1.171686173
Mean0.1604745519
Median Absolute Deviation (MAD)0.000197
Skewness1.695866039
Sum27168.66258
Variance0.0948617973
MonotocityNot monotonic
2020-09-16T21:32:56.087072image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
04605527.2%
 
0.9171920.1%
 
0.9161900.1%
 
0.9131860.1%
 
0.9041790.1%
 
0.9011770.1%
 
0.9221730.1%
 
0.8941710.1%
 
0.9141710.1%
 
0.9031700.1%
 
Other values (5391)12163871.8%
 
ValueCountFrequency (%) 
04605527.2%
 
1e-0628< 0.1%
 
1.01e-0667< 0.1%
 
1.02e-06890.1%
 
1.03e-0670< 0.1%
 
ValueCountFrequency (%) 
110< 0.1%
 
0.99913< 0.1%
 
0.99810< 0.1%
 
0.9973< 0.1%
 
0.9966< 0.1%
 

loudness
Real number (ℝ)

Distinct25313
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.34211645
Minimum-60
Maximum3.855
Zeros0
Zeros (%)0.0%
Memory size1.3 MiB
2020-09-16T21:32:56.234469image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-21.80895
Q1-14.42975
median-10.453
Q3-7.109
95-th percentile-4.09305
Maximum3.855
Range63.855
Interquartile range (IQR)7.32075

Descriptive statistics

Standard deviation5.642668316
Coefficient of variation (CV)-0.4974969476
Kurtosis1.932477728
Mean-11.34211645
Median Absolute Deviation (MAD)3.59
Skewness-1.071872125
Sum-1920243
Variance31.83970572
MonotocityNot monotonic
2020-09-16T21:32:56.369669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
-7.00627< 0.1%
 
-7.43627< 0.1%
 
-7.63226< 0.1%
 
-9.29826< 0.1%
 
-8.3226< 0.1%
 
-6.94226< 0.1%
 
-11.81525< 0.1%
 
-7.56625< 0.1%
 
-11.45125< 0.1%
 
-8.78925< 0.1%
 
Other values (25303)16904499.8%
 
ValueCountFrequency (%) 
-609< 0.1%
 
-551< 0.1%
 
-54.3761< 0.1%
 
-51.1231< 0.1%
 
-51.081< 0.1%
 
ValueCountFrequency (%) 
3.8551< 0.1%
 
3.7441< 0.1%
 
2.7991< 0.1%
 
1.9631< 0.1%
 
1.831< 0.1%
 

speechiness
Real number (ℝ≥0)

Distinct1628
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09419094281
Minimum0
Maximum0.969
Zeros147
Zeros (%)0.1%
Memory size1.3 MiB
2020-09-16T21:32:56.516622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0281
Q10.0349
median0.0451
Q30.0755
95-th percentile0.332
Maximum0.969
Range0.969
Interquartile range (IQR)0.0406

Descriptive statistics

Standard deviation0.1501470001
Coefficient of variation (CV)1.594070466
Kurtosis19.30765722
Mean0.09419094281
Median Absolute Deviation (MAD)0.0131
Skewness4.229128896
Sum15946.715
Variance0.02254412163
MonotocityNot monotonic
2020-09-16T21:32:56.660732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.03475710.3%
 
0.03195630.3%
 
0.03335580.3%
 
0.03375570.3%
 
0.03345550.3%
 
0.03525540.3%
 
0.03325510.3%
 
0.03155490.3%
 
0.03365480.3%
 
0.0345480.3%
 
Other values (1618)16374896.7%
 
ValueCountFrequency (%) 
01470.1%
 
0.02221< 0.1%
 
0.02233< 0.1%
 
0.02245< 0.1%
 
0.02255< 0.1%
 
ValueCountFrequency (%) 
0.9691< 0.1%
 
0.9683< 0.1%
 
0.9673< 0.1%
 
0.96617< 0.1%
 
0.96522< 0.1%
 

tempo
Real number (ℝ≥0)

Distinct84548
Distinct (%)49.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.9904707
Minimum0
Maximum244.091
Zeros146
Zeros (%)0.1%
Memory size1.3 MiB
2020-09-16T21:32:56.825964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile74.2402
Q193.5885
median114.8315
Q3135.7445
95-th percentile174.4717
Maximum244.091
Range244.091
Interquartile range (IQR)42.156

Descriptive statistics

Standard deviation30.71802952
Coefficient of variation (CV)0.2625686463
Kurtosis-0.07618982086
Mean116.9904707
Median Absolute Deviation (MAD)21.0875
Skewness0.4485858734
Sum19806720.67
Variance943.5973373
MonotocityNot monotonic
2020-09-16T21:32:56.964333image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
01460.1%
 
12021< 0.1%
 
119.98918< 0.1%
 
120.00517< 0.1%
 
119.99417< 0.1%
 
119.96917< 0.1%
 
120.01217< 0.1%
 
129.99516< 0.1%
 
120.01116< 0.1%
 
95.02715< 0.1%
 
Other values (84538)16900299.8%
 
ValueCountFrequency (%) 
01460.1%
 
30.9461< 0.1%
 
31.9881< 0.1%
 
32.4661< 0.1%
 
32.81< 0.1%
 
ValueCountFrequency (%) 
244.0911< 0.1%
 
243.5071< 0.1%
 
243.3721< 0.1%
 
238.8951< 0.1%
 
236.7991< 0.1%
 

valence
Real number (ℝ≥0)

Distinct1739
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5329432862
Minimum0
Maximum1
Zeros183
Zeros (%)0.1%
Memory size1.3 MiB
2020-09-16T21:32:57.107593image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0938
Q10.324
median0.545
Q30.75
95-th percentile0.938
Maximum1
Range1
Interquartile range (IQR)0.426

Descriptive statistics

Standard deviation0.2621413115
Coefficient of variation (CV)0.4918746859
Kurtosis-1.048579821
Mean0.5329432862
Median Absolute Deviation (MAD)0.213
Skewness-0.1267632558
Sum90228.36424
Variance0.0687180672
MonotocityNot monotonic
2020-09-16T21:32:57.247680image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.9617250.4%
 
0.9625970.4%
 
0.9635260.3%
 
0.9644670.3%
 
0.9654050.2%
 
0.963880.2%
 
0.9663550.2%
 
0.9673110.2%
 
0.9682700.2%
 
0.5592520.1%
 
Other values (1729)16500697.5%
 
ValueCountFrequency (%) 
01830.1%
 
1e-0575< 0.1%
 
6.41e-051< 0.1%
 
0.000491< 0.1%
 
0.0005371< 0.1%
 
ValueCountFrequency (%) 
13< 0.1%
 
0.9991< 0.1%
 
0.9981< 0.1%
 
0.9962< 0.1%
 
0.9951< 0.1%
 

mode
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
1
119979 
0
49323 
ValueCountFrequency (%) 
111997970.9%
 
04932329.1%
 
2020-09-16T21:32:57.340139image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

key
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.199761373
Minimum0
Maximum11
Zeros21431
Zeros (%)12.7%
Memory size1.3 MiB
2020-09-16T21:32:57.409448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.51507068
Coefficient of variation (CV)0.6760061524
Kurtosis-1.272421934
Mean5.199761373
Median Absolute Deviation (MAD)3
Skewness0.004988843093
Sum880330
Variance12.35572188
MonotocityNot monotonic
2020-09-16T21:32:57.502266image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%) 
02143112.7%
 
72069712.2%
 
21876011.1%
 
91758010.4%
 
5162799.6%
 
4128817.6%
 
1127757.5%
 
10119847.1%
 
8106726.3%
 
11105526.2%
 
Other values (2)156919.3%
 
ValueCountFrequency (%) 
02143112.7%
 
1127757.5%
 
21876011.1%
 
371394.2%
 
4128817.6%
 
ValueCountFrequency (%) 
11105526.2%
 
10119847.1%
 
91758010.4%
 
8106726.3%
 
72069712.2%
 

popularity
Real number (ℝ≥0)

ZEROS

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.65795442
Minimum0
Maximum100
Zeros26802
Zeros (%)15.8%
Memory size1.3 MiB
2020-09-16T21:32:57.622701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median34
Q348
95-th percentile65
Maximum100
Range100
Interquartile range (IQR)36

Descriptive statistics

Standard deviation21.54183438
Coefficient of variation (CV)0.6804556636
Kurtosis-1.008177751
Mean31.65795442
Median Absolute Deviation (MAD)16
Skewness-0.02573882078
Sum5359755
Variance464.0506287
MonotocityNot monotonic
2020-09-16T21:32:57.764385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
02680215.8%
 
4232801.9%
 
4331201.8%
 
4030611.8%
 
4430531.8%
 
4130161.8%
 
4529441.7%
 
3829001.7%
 
3928681.7%
 
3528581.7%
 
Other values (90)11540068.2%
 
ValueCountFrequency (%) 
02680215.8%
 
122481.3%
 
214480.9%
 
312000.7%
 
410740.6%
 
ValueCountFrequency (%) 
1001< 0.1%
 
991< 0.1%
 
971< 0.1%
 
961< 0.1%
 
954< 0.1%
 

explicit
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.3 MiB
0
154890 
1
 
14412
ValueCountFrequency (%) 
015489091.5%
 
1144128.5%
 
2020-09-16T21:32:57.859175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Interactions

2020-09-16T21:32:23.600489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:23.760399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:23.903188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.051838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.201073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.349432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.494856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.642849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.792094image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.940587image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:25.204828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:25.352872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:25.497574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:25.641384image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:25.786618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:25.923444image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.063677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.204508image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.345129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.484309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.624627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.765491image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.905866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.046302image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.186557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.323999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.459798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.606455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.744289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.884001image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.023800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.165525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.303280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.443026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.584565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.728893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.868767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.007878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.143902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.280895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.427141image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.565342image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.706805image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.846065image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.986716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:30.242703image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:30.383688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:30.525966image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:30.667846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:30.807937image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:30.947932image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.084461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.221347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.367963image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.506655image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.647633image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.788014image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.927891image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.065296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.206892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.349449image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.490953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.632069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.772925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.910271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.046543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.193090image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.332612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.471390image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.610411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.750659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.886770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.026507image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.168075image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.312907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.456887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.595328image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.731733image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.866826image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.014078image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.152969image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.293704image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.433457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.579412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.719231image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.859291image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.002225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.141720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.425711image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.565354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.703159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.839785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.997082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:37.148605image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:37.298964image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:37.441920image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:37.581109image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:37.720185image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:37.862791image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.007019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.149979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.292020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.461148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.621634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.773105image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.949614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:39.102745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:39.247312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:39.395386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:39.595959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:40.066278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:40.284885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:40.482327image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:40.699453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:40.852272image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.058800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.210802image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.351634image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.513145image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.664130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.808025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.951872image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.102296image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.243885image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.383506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.524901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.670851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.817502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.967177image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.108224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.250059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.402571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.548793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.689347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.838058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.987059image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:44.158782image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:44.396946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:44.579710image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:44.720195image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.076459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.222089image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.364448image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.515976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.667412image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.818787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.959657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.109210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.253717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.391085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.541688image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.691194image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.841018image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.989888image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.128411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.263661image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.399865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.546315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.687341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.827892image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.966915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:48.111809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:48.315902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:48.621879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:48.792752image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:48.952940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:49.309397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:49.621745image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:49.791870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-09-16T21:32:57.946834image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-16T21:32:58.180197image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-16T21:32:58.441702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-16T21:32:58.712200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-16T21:32:50.321632image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:50.989530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexnameartistsduration_msyearacousticnessdanceabilityenergyinstrumentalnessloudnessspeechinesstempovalencemodekeypopularityexplicit
00Singende Bataillone 1. Teil['Carl Woitschach']15864819280.9950.7080.19500.563-12.4280.0506118.4690.779011000
11Fantasiestücke, Op. 111: Più tosto lento['Robert Schumann', 'Vladimir Horowitz']28213319280.9940.3790.01350.901-28.4540.046283.9720.07671800
22Chapter 1.18 - Zamek kaniowski['Seweryn Goszczyński']10430019280.6040.7490.22000.000-19.9240.9290107.1770.88000500
33Bebamos Juntos - Instrumental (Remasterizado)['Francisco Canaro']18076019280.9950.7810.13000.887-14.7340.0926108.0030.72000100
44Polonaise-Fantaisie in A-Flat Major, Op. 61['Frédéric Chopin', 'Vladimir Horowitz']68773319280.9900.2100.20400.908-16.8290.042462.1490.069311110
55Scherzo a capriccio: Presto['Felix Mendelssohn', 'Vladimir Horowitz']35260019280.9950.4240.12000.911-19.2420.059363.5210.26600600
66Valse oubliée No. 1 in F-Sharp Major, S. 215/1['Franz Liszt', 'Vladimir Horowitz']13662719280.9560.4440.19700.435-17.2260.040080.4950.305011100
77Per aspera ad astra['Carl Woitschach']15396719280.9880.5550.42100.836-9.8780.0474123.3100.85701100
88Moneda Corriente - Remasterizado['Francisco Canaro', 'Charlo']16249319280.9950.6830.20700.206-9.8010.1270119.8330.49300900
99Chapter 1.3 - Zamek kaniowski['Seweryn Goszczyński']11160019280.8460.6740.20500.000-20.1190.954081.2490.75901900

Last rows

df_indexnameartistsduration_msyearacousticnessdanceabilityenergyinstrumentalnessloudnessspeechinesstempovalencemodekeypopularityexplicit
169292169899Rough Ryder['YoungBoy Never Broke Again']16101920200.37100.6230.7210.000000-4.5840.3390166.6370.719010641
169293169900I Dare You['Kelly Clarkson']21610720200.04520.6550.7190.000018-7.4000.0368124.0340.43512690
169294169901Letter To Nipsey (feat. Roddy Ricch)['Meek Mill', 'Roddy Ricch']16784520200.26400.7440.7020.000000-6.2550.288091.8850.33807661
169295169902Back Home (feat. Summer Walker)['Trey Songz', 'Summer Walker']19457620200.02270.6190.7190.000000-4.1110.157086.0360.35110691
169296169903Ojos De Maniaco['LEGADO 7', 'Junior H']21850120200.21000.7950.5850.000001-4.4510.037497.4790.93418680
169297169904Skechers (feat. Tyga) - Remix['DripReport', 'Tyga']16380020200.17300.8750.4430.000032-7.4610.1430100.0120.30611751
169298169905Sweeter (feat. Terrace Martin)['Leon Bridges', 'Terrace Martin']16746820200.01670.7190.3850.031300-10.9070.0403128.0000.27018640
169299169906How Would I Know['Kygo', 'Oh Wonder']18070020200.53800.5140.5390.002330-9.3320.1050123.7000.15317700
169300169907I Found You['Cash Cash', 'Andy Grammer']16730820200.07140.6460.7610.000000-2.5570.0385129.9160.47211700
169301169908More Hearts Than Mine['Ingrid Andress']21478720200.10900.5120.4280.000000-7.3870.027180.5880.36610650